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The paper empirically characterizes the learning geometry of hybrid quantum forecasting models, comparing them to classical baselines using Neural Tangent Kernel dynamics and other metrics, showing that similar generalization can emerge from different optimization trajectories.
This paper develops a sharp pseudospectral theory for block-triangular Jacobians in coupled gradient descent, proving Kreiss-constant bounds and establishing iteration complexity results. The work exposes non-asymptotic, instance-dependent transient amplification phenomena relevant to bilevel optimization, two-time-scale stochastic approximation, and GAN training.
This paper proposes the 'support-before-frequency' hypothesis for discrete diffusion models, suggesting that models first learn the support (admissible sequences) before refining frequencies within the support. Theoretical analysis of small-noise reverse kernels and experiments on masked language diffusion models support this claim.
This paper develops a local theory of gradient descent near bifurcations in dynamical models, showing that the state-space neural tangent kernel collapses to a rank-one operator that dominates learning dynamics, making optimization effectively low-dimensional and predictable from normal forms.